Tag Archives: Social Science

Henry Farrell on Age of Em

There is a difference between predicting the weather, and predicting climate. If you know many details on current air pressures, wind speeds, etc, you can predict the weather nearby a few days forward, but after weeks to months at most you basically only know an overall distribution. However, if there is some fundamental change in the environment, such as via carbon emissions, you might predict how that distribution will change as a result far into the future; that is predicting climate.

Henry Farrell, at Crooked Timber, seems to disagree with Age of Em because he thinks we can only predict social weather, not social climate:

Tyler Cowen says .. Age of Em .. won’t happen. I agree. I enjoyed the book. .. First – the book makes a strong claim for the value of social science in extrapolating likely futures. I am a lot more skeptical that social science can help you make predictions. .. Hanson’s arguments seem to me to rely on a specific combination of (a) an application of evolutionary theory to social development with (b) the notion that evolutionary solutions will rapidly converge on globally efficient outcomes. This is a common set of assumptions among economists with evolutionary predilections, but it seems to me to be implausible. In actually existing markets, we see some limited convergence in the short term on e.g. forms of organization, but this is plausibly driven at least as much by homophily and politics as by the actual identification of efficient solutions. Evolutionary forces may indeed lead to the discovery of new equilibria, but haltingly, and in unexpected ways. .. This suggests an approach to social science which doesn’t aim at specific predictions a la Hanson, so much as at identifying the underlying forces which interact (often in unpredictable ways) to shape and constrain the range of possible futures. ..

In the end, much science fiction is doing the same kind of thing as Hanson ends up doing – trying in a reasonably systematic way to think through the social, economic and political consequences of certain trends, should they develop in particular ways. The aims of extrapolationistas and science fiction writers aims may be different – prediction versus constrained fiction writing but their end result – enriching our sense of the range of possible futures that might be out there – are pretty close to each other. .. it is the reason I got value from his book. ..

So Hanson’s extrapolated future seems to me to reflect an economist’s perspective in which markets have priority, and hierarchy is either subordinated to the market or pushed aside altogether. The work of Hannu Rajaniemi provides a rich, detailed, alternative account of the future in which something like the opposite is true .. [with] vast and distributed hierarchies of exploitation. .. Rajaniemi’s books .. provide a rich counter-extrapolation of what a profoundly different society might look like. .. I don’t know what the future will look like, but I suspect it will be weird in ways that echo Rajaniemi’s way of thinking (which generates complexities) rather than Hanson’s (which breaks them down).

If we can only see forces that shape and constrain the future, but not the distribution of future outcomes, what is the point of looking at samples from the “range of possibilities”? That only seems useful if in fact you can learn things about that range. In which case you are learning about the overall distribution. Isn’t Farrell’s claim about more future “hierarchies of exploitation” relative to “markets” just the sort of overall outcome he claims we can’t know? (Rajaniemi blurbed and likes my book, so I don’t think he sees it as such a polar opposite. And how does hierarchy “generate complexities” while markets “break them down”?) Is Farrell really claiming that there is no overall tendency toward more efficient practices and institutions, making moves away from them just as likely as moves toward them? Are all the insights economic historians think they have gained using efficiency to understand history illusory?

My more charitable interpretation is that Farrell sees me as making forecasts much more confidently than I intend. While I’ve constructed a point prediction, my uncertainty is widely distributed around that point, while Farrell sees me as claiming more concentration. I’ll bet Farrell does in fact see a tendency toward efficiency, and he thinks looking at cases does teach us about distributions. And he probably even thinks supply and demand is often a reasonable first cut approximation. So I’m guessing that, with the right caveat about confidence, he actually thinks my point prediction makes a useful contribution to our understanding of the future.

One clarification. Farrell writes:

One of the unresolved tensions .. Are [ems] free agents, or are they slaves? I don’t think that Hanson’s answer is entirely consistent (or at least I wasn’t able to follow the thread of the consistent argument if it was). Sometimes he seems to suggest that they will have successful means of figuring out if they have been enslaved, and refusing to cooperate, hence leading to a likely convergence on free-ish market relations. Other times, he seems to suggest that it doesn’t make much difference to his broad predictive argument whether they are or are not slaves.

Much of the book doesn’t depend on if ems are slaves, but some parts do, such as the part on how ems might try to detect if they’ve been unwittingly enslaved.

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Star Trek As Fantasy

Frustrated that science fiction rarely makes economic sense, I just wrote a whole book trying to show how much consistent social detail one can offer, given key defining assumptions on a future scenario. Imagine my surprise then to learn that another book, Trekonomics, published exactly one day before mine, promises to make detailed economic sense out of the popular Star Trek shows. It seems endorsed by top economists Paul Krugman and Brad Delong, and has lots of MSM praise. From the jacket:

Manu Saadia takes a deep dive into the show’s most radical and provocative aspect: its detailed and consistent economic wisdom. .. looks at the hard economics that underpin the series’ ideal society.

Now Saadia does admit the space stuff is “hogwash”:

There will not be faster-than-light interstellar travel or matter-anti-matter reactors. Star Trek will not come to pass as seen on TV. .. There is no economic rationale for interstellar exploration, maned or unmanned. .. Settling a minuscule outpost on a faraway  world, sounds like complete idiocy. .. Interstellar exploration … cannot happen until society is so wealthy that not a single person has to waste his or her time on base economic pursuits. .. For a long while, there is no future but on Earth, in the cities of Earth. (pp. 215-221)

He says Trek is instead a sermon promoting social democracy: Continue reading "Star Trek As Fantasy" »

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Seeking Super Factors

In a factor analysis, one takes a large high-dimensional dataset and finds a low dimensional set of variables that can explain as much as possible of the total variation in that dataset. A big advantage of factor analysis is that it doesn’t require much theoretical knowledge about the nature of the variables in the data or their relations – factors are mostly determined directly by the data.

Factor analysis has had some big successes in helping us to understand how humans differ. As many people know, intelligence is the main factor explaining variation in cognitive test performance, ideology is the main factor explaining variations in political positions, and personality types explain much of the variation in stable attitudes and temperament. These factors have allowed us to greatly advance our understanding of intelligence, ideology, and personality, even while remaining ignorant of their fundamental causes and natures.

However, people vary in far more ways than intelligence, ideology, and personality, and factor analyses have been applied to many of these other human feature categories. For example, there have been factors analyses of jobs, brands, faces, body shape, gait, accent, diet, clothing, writing styleleisure behavior, friendship networks, sleep habitsphysical health, mortality, demography, national cultures, and zip codes.

As my last post on media genre factors showed, factors found in different feature categories are often substantially correlated with one another. This suggests that if we put together a huge super-dataset describing many individual people in as many ways as possible, a factor analysis of this dataset may find important new super-factors that span many of these features domains. Such super-factors would be promising candidates to use in a wide range of social research, and social policy.

Now it remains logically possible that these super-factors will end up being simple linear combinations of the factors that we have already found in each of these feature categories. Maybe we already know most of what there is to know about how humans vary. But I’d bet strongly and heavily against this. The rate at which we have been learning new things about how humans vary doesn’t remotely suggest we’ve run out of new big things to learn. Yes, merely knowing the super-factors isn’t the same as understanding their origins. But just as we’ve seen with factor analysis in more specific areas, knowing the main factors can be a big help.

So I’d guess that the super-factors found in a super dataset of human details will be revolutionary developments. We will afterward see uncovering them as a seminal milestone in our progress in understanding human variation. A Nobel prize worthy level of seminality. All it will take is lots of tedious work to collect a super dataset, and then do some straightforward number crunching. A quest awaits; who will rise to the challenge?

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Em Scale Economies

Angle, a relaunched journal from Imperial College London, “focuses on the intersection of science, policy and politics in an evolving and complex world.” The current issue focuses on economies of scale, and includes a short paper of mine on ems:

I focus on two key results related to economies of scale. … First, an em economy grows faster that ours by avoiding the diminishing returns to capital that we suffer because we can’t grow labour fast enough. Second, an economy has larger cities because it avoids the commuting congestion costs that limit our city sizes. (more)

Of course an em economy has many other important scale economies; those where just the two I could explain in the two thousand words given me.

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The Data We Need

Almost all research into human behavior focuses on particular behaviors. (Yes, not extremely particular, but also not extremely general.) For example, an academic journal article might focus on professional licensing of dentists, incentive contracts for teachers, how Walmart changes small towns, whether diabetes patients take their medicine, how much we spend on xmas presents, or if there are fewer modern wars between democracies. Academics become experts in such particular areas.

After people have read many articles on many particular kinds of human behavior, they often express opinions about larger aggregates of human behavior. They say that government policy tends to favor the rich, that people would be happier with less government, that the young don’t listen enough to the old, that supply and demand is a good first approximation, that people are more selfish than they claim, or that most people do most things with an eye to signaling. Yes, people often express opinions on these broader subjects before they read many articles, and their opinions change suspiciously little as a result of reading many articles. But even so, if asked to justify their more general views academics usually point to a sampling of particular articles.

Much of my intellectual life in the last decade has been spent in the mode of collecting many specific results, and trying to fit them into larger simpler pictures of human behavior. So both I and the academics I’m describing above in essence present themselves as using these many results presented in academic papers about particular human behaviors as data to support their broader inferences about human behavior. But we do almost all of this informally, via our vague impressionistic memories of what has been the gist of the many articles we’ve read, and our intuitions about what more general claims seem how consistent with those particulars.

Of course there is nothing especially wrong with intuitively matching data and theory; it is what we humans evolved to do, and we wouldn’t be such a successful species if we couldn’t at least do it tolerably well sometimes. It takes time and effort to turn complex experiences into precise sharable data sets, and to turn our theoretical intuitions into precise testable formal theories. Such efforts aren’t always worth the bother.

But most of these academic papers on particular human behaviors do in fact pay the bother to substantially formalize their data, their theories, or both. And if it is worth the bother to do this for all of these particular behaviors, it is hard to see why it isn’t be worth the bother for the broader generalizations we make from them. Thus I propose: let’s create formal data sets where the data points are particular categories of human behavior.

To make my proposal clearer let’s for now restrict attention to explaining government regulatory policies. We could create a data set where the datums are particular kinds of products and services that governments now provide, subsidize, tax, advise, restrict, etc. For such datums we could start to collect features about them into a formal data set. Such features could say how long that sort of thing has been going on, how widely it is practiced around the world, how variable has been that practice over space and time, how familiar are ordinary people today with its details, what sort of justifications do people offer for it, what sort of emotional associations do people have with it, how much do we spend on it, and so on. We might also include anything we know about how such things correlate with age, gender, wealth, latitude, etc.

Generalizing to human behavior more broadly, we could collect a data set of particular behaviors, many of which seem puzzling at least to someone. I often post on this blog about puzzling behaviors. Each such category of behaviors could be one or more data points in this data set. And relevant features to code about those behaviors could be drawn from the features we tend to invoke when we try to explain those behaviors. Such as how common is that behavior, how much repeated experience do people have with it, how much do they get to see about the behavior of others, how strong are the emotional associations, how much would it make people look bad to admit to particular motives, and so on.

Now all this is of course much easier said than done. Is it a lot of work to look up various papers and summarize their key results as entries in this data set, or just to look at real world behaviors and put them into simple categories. It is also work to think carefully about how to usefully divide up the space of actions and features. First efforts will no doubt get it wrong in part, and have to be partially redone. But this is the sort of work that usually goes into all the academic papers on particular behaviors. Yes it is work, but if those particular efforts are worth the bother, then this should be as well.

As a first cut, I’d suggest just picking some more limited category, such as perhaps government regulations, collecting some plausible data points, making some guesses about what useful features might be, and then just doing a quick survey of some social scientists where they each fill in the data table with their best guesses for data point features. If you ask enough people, you can average out a lot of individual noise, and at least have a data set about what social scientists think are features of items in this area. With this you could start to do some exploratory data analysis, and start to think about what theories might well account for the patterns you see.

Now one obvious problem with my proposal is that while it looks time consuming and tedious, it isn’t obviously impressive. Researchers who specialize in particular areas will complain about your data entries related to their areas, and you won’t be able to satisfy them all. So you will end up with a chorus of critics saying your data is all wrong, and your efforts will look too low brow to cower them with your impressive tech. So I can see why this hasn’t been done much. Even so, I think this is the data set we need.

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Yawning At Utopia

The prospect of better physical devices, such as logic gates or solar cells, often generates huge interest and investment. Of course there are many more physical devices where improvements generate much less interest, because we haven’t yet found nearly as much use for those devices. But even so, for devices we often use, small improvements can be very big news.

Similarly, there are many widely used computer algorithms where small improvements also generate big interest and financial investments. Of course most gains aren’t like this. For example, there is less interest in techniques tied to very narrow contexts, such as ways to reorganize particular programs. But when wide use is plausible, algorithm gains can be big news.

We can do engineering and design not only with physical and software systems, but also with social systems. There should of course be less interest in designs tied to very particular contexts, such as reorganizing the management of a particular firm. But we often repeatedly use some simple social mechanisms, like voting. So we should be a lot more interest in improving the designs of these.

I started out in engineering, moved to physics, then to software, and then finally to economics. That last move was very much inspired by big apparent gains from better social institutions. I knew that in physical and software engineering we put in huge efforts to scour the vast space of possible designs to find even small gains on devices of moderate generality. Yet in economics it seemed that big gains could be found from very simple easy to find innovations on general mechanisms of wide applicability.

Over two decades later, I must admit that the world shows far less interest in better designs for institutions and social mechanisms, relative to better designs for physical and software systems. Few talk about them, and even fewer business ventures pursue them. Some say that physics and software designs are far more valuable because we know far less about economics; these proposed social designs just don’t work. But this claim seems just wrong to me.

Yes of course any particular argument for any particular social design will make convenient but questionable assumptions. But this is also true for our main arguments for physical or software designs. They also almost always neglect relevant considerations. Tractable analysis simplifies.

I recently posted on a new voting mechanism. Voting is a very general process whose main purposes are also pretty general. I’ve also posted for years about the very general advantages of prediction markets for the problem of info aggregation, which is a very general problem. (Scott Sumner sees their gains as so obvious he calls anything else “Stone Age Economics”.) I just heard a nice talk on better political institutions to promote urban density. And economic journals are full of articles describing new institution designs, and testing the effects of institutions that are not widely adopted.

Yes, proposed new social mechanisms often fail along the path from simple theory models to complex models to lab experiments to small field experiments to large field trials. But physical and software designs also often fail along this path. I don’t see social designs as failing much more often, except for the key failing of not generating much enthusiasm or interest. That is, most people just don’t seem to care how well social designs do in theory or lab or field tests. Even most social scientists don’t care much about design innovations outside their specialty areas.

Yes in the last decade or so there has been more enthusiasm for social innovations embodied in physical and software innovations, like smart phones or block chains. But this enthusiasm seems to be mainly an accidental side effect of tech enthusiasm. For example, while many are excited by Uber achieving new value in cheaper-if-nominally-illegal cab services, most of those gains could have come decades ago from just deregulating cabs, an option in which there was little interest. As another example, there is far more interest today in prediction markets build on block chains than in ordinary prediction markets, even though far more value could be achieved by the later.

I should admit that this all confirms Bryan Caplan’s claim that few people can generate much emotional enthusiasm for efficiency. Bryan says people are far more engaged by moral arguments. I’d say people are also far more engaged by following fashion and by us vs. them coalition politics. Most apparent interest in innovation in social designs can be attributed to these three sources; we explain little more by positing an additional direct interest in helping us all get more of what we want.

This seems mostly also true at the level of smaller organizations like firms. While people give lip service to increasing the efficiency or effectiveness of the organization as a whole, that in fact generates little passion. The passion we do see in the name of efficiency mostly advances particular factions and individual careers. Homo hypocritus is quite skilled at saying that he serves the great good, while actually serving far more personal ends.

Added 9a: Many of you seem to be stuck on the ideas that social innovations can’t be tested unless the entire world agrees to adopt them. Or an entire nation, or city. Yes, some innovations are like that. (There are also physical and software innovations like that.) But a great many social innovations can be tried out on very small scales, where regulations do not block them. And there is very little interest in pursuing these innovations.

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Do Economists Care?

Art Carden:

Heavy traffic is a problem every economist in the world knows how to solve: price road access, and charge high prices during rush hour. With technologies like E-ZPass and mobile apps, it’s easier than ever. That we don’t pick this low-hanging fruit is a pretty serious indictment of public policy. If we can’t address what is literally a principles-level textbook example of a negative spillover with a fairly easy fix, what hope do we have for effective public policy on other margins? (more)

Yes! If economists actually cared about influencing real policy, they would:

  1. Identify a few strong candidate policies that are a) widely endorsed by economists, b) based on relatively simple clean analysis, c) not much adopted in the wider world, and d) should bring big gains.
  2. Try to engage other intellectuals in detail on one or a few of these, seeking to either gain their endorsement, or to understand better the barriers that block them. If possible, do this as a group, and using all our status levers to make them respond in detail. If we succeed in persuading intellectuals, then join with them to try to persuade policy-makers, again either succeeding or better understanding barriers.
  3. Once we better understand barriers, focus our economic research on doing what it takes to overcome them.

By not doing this, we basically say that while we think we know how to make a better world, we don’t much care if that happens; our priorities are elsewhere.

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Social Science Exists

A student with a mathematical-physics background could easily convince himself that he has superior mathematics abilities than typical economists and superior statistical and computational skills than most economists. He might go on to conclude that, as a consequence of his superior mathematical and computational abilities, he should be able to enter economics and start contributing quickly and easily. He might also anticipate that he could easily adapt established models or techniques in physics to study economic phenomena and impress the profession.

If you are one of these people, let me try to disabuse you of these notions. Your mathematical abilities are actually not that much better than most economists (if they are better at all). You will have to spend a lot of time acclimating to the subject and the path to actually making contributions will be long and difficult. In all likelihood, there are very few (perhaps zero) off-the-shelf models or techniques in physics (or engineering, or chemistry, …) that will produce meaningful economic results. (more; HT Justin Wolfers)

Yup! I often meet scientist types who talk about some problem they are working on, which turns out to be a social problem related to ones that social scientists have explored. But they won’t believe this unless you show them work that uses methods and concepts with which they are familiar. They just can’t believe there are useful methods and concepts that they don’t already know.

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Dust In The Wind

All we are is dust in the wind. (Song lyrics)

Alex:

Contra Tyler, the lesson of history is that few things are as effective at launching a revolution as is moral argument. Without the firebrand Thomas “We have it in our power to begin the world over again“ Paine, the American Revolution would probably never have happened. (more)

Imagine standing at the shore of a river. You scoop a handful of water, and throw it downstream. By how much do you expect that act to change the flow of the river into the ocean miles downstream? I expect the effect to be far less than a handful of water arriving a few seconds earlier. More like a few atoms arriving a few seconds earlier. The speed of a river is a balance between gravity and friction, and that balance is likely to be quickly restored after disturbances like throwing a handful of water.

This seems a pretty typical example of influencing the physical world. The vast majority of such influences quickly disappear. So if you want your influence to last, you have to choose carefully. For example, since on Earth nature only rarely moves big stones, you might succeed in assembling a stone wall that lasts for thousands of years. At least if other people don’t want to knock it down.

Now consider trying to have a long term social influence. As with physical influence, we should expect that most efforts to influence the social world also diminish quickly away from the point of influence. After all, many aspects of the social world also result from balances between opposing forces. For example, if US independence was largely inevitable in the long run, then Thomas Paine could have at most influenced when exactly when the US became independent.

But what if there are tipping points? Imagine that a burst of floodwater came to the edge of overflowing a dam. An overflow might dig a channel leading in a new direction, changing the course of a river for a long time to come. So adding or subtracting just a little water near that overflow point might have a big long term effect. Can this metaphor give us more hope for long term social influence?

Well first, such tipping points must be rare – the vast majority of points can’t tip very far. Second, when many people can influence a social event, not only are most people only a drop in a tide of influence, most people are also only a drop in a tide of information. For example, imagine that people were pushing for or against US independence based on their best info on if that is good for the world. In this case Paine could only be in a position to tip the outcome if many other people also could tip the outcome, and if they were pushing in many different directions, with their net effects nearly balancing out.

In a case like this, Paine couldn’t be at all sure that a US revolution was a good idea. After all, an awful lot of people would have best info suggesting it was not a good idea. And in fact Bryan Caplan makes a good case that it wasn’t in fact a good idea.

Of course many people might have been pushing based on private interests, instead of a common good. But this still wouldn’t give Paine much reason for confidence in his tipping the world to a better place. Either many others would try to help the world, or Paine couldn’t have good reason to think he is the only exception.

So are there any good ways to have long term influence? One idea is to find a social situation like the stone wall, where you can add things that aren’t likely to get moved, and where your stones aren’t likely to be added anyway a bit later by someone else. Perhaps doing intellectual work on highly neglected topics is something like this.

See also: Long Legacies

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Who Care About Econ Errors?

An economist would rightly lambasted for writing a popular article where perpetual motion machines, or anti-gravity, were central elements. Especially if that economist didn’t even notice that these violate well-established scientific consensus.

But famed Princeton neuroscientist, novelist, and composer Michael Graziano has a popular article that errs nearly as badly in its economics. And, alas, I doubt many will consider that worthy of lambasting, or that Graziano will feel embarrassed by it. Or even consider the issue worthy of comment.

Graziano’s economics error is to focus entirely on demand-side effects, and completely ignore supply-side effects, when summarizing the social implications of brain emulations. That is, he apparently can only see using brain emulations as a way to make a “virtual afterlife” vacation-land. So he talks only about who gets in and what they do there. He can’t even seem to imagine that emulated brains might be useful workers, and so drastically change the world outside of virtual heavens. (A subject I’ve analyzed in depth.) Read for yourself:

Endless fun  The question is not whether we can upload our brains onto a computer, but what will become of us when we do. …

For nearly 30 years, I’ve studied how sensory information gets taken in and processed, how movements are controlled and, lately, how networks of neurons might compute the spooky property of awareness. I find myself asking, given what we know about the brain, whether we really could upload someone’s mind to a computer. And my best guess is: yes, almost certainly. That raises a host of further questions, not least: what will this technology do to us psychologically and culturally? Here, the answer seems just as emphatic, if necessarily murky in the details. …

People … don’t like to die. … Some of them already pay enormous sums to freeze themselves. … These kinds of people will certainly pay for a spot in a virtual afterlife. … Think of the fun to be had as a simulated you in a simulated environment. You could go on a safari through Middle Earth. You could live in Hogwarts. … You could keep in touch with your living friends through all the usual social media. …

We will tend to treat human life and death much more casually. People will be more willing to put themselves and others in danger. … Will simulated people, living in an artificial world, have the same human rights as the rest of us? … Who decides who gets in? … issues … will arise if people deliberately run multiple copies of themselves at the same time. … Do [married couples] stay together? …

Two people will be able to join thoughts directly with each other. … Pretty soon everyone is linked mind-to-mind. The concept of separate identity is lost. The need for simulated bodies walking in a simulated world is lost. The need for simulated food and simulated landscapes and simulated voices disappears. Instead, a single platform of thought, knowledge, and constant realisation emerges. … Real life, our life, will shrink in importance until it becomes a kind of larval phase … I am not talking about utopia. To me, this prospect is three parts intriguing and seven parts horrifying. I am genuinely glad I won’t be around. (more)

So why won’t Graziano won’t be embarrassed by this? Because his colleagues won’t see it as valid criticism, because most don’t think economics really exists as a source of reliable insight.

Btw, I doubt that even in a virtual heaven most people would want to spend much time hooked up directly to share each other’s thoughts in depth. Our minds aren’t designed for that, and i doubt simple modifications can make that work well. And I’m even more skeptical that productive working ems would typically be hooked up this way.

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